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Hassel and Marianne Ledbetter MatSE Colloquium - "Materials informatics: Moving beyond screening via generative machine learning models"

Event Type
Seminar/Symposium
Sponsor
Department of Materials Science and Engineering
Location
100 Materials Science and Engineering Building, 1304 W. Green Street
Date
Feb 19, 2024   4:00 pm  
Speaker
Taylor Sparks, Department of Materials Science and Engineering, University of Utah
Contact
Bailey Peters
E-Mail
bnpeters@illinois.edu
Phone
217-333-1441
Views
38
Originating Calendar
MatSE Colloquium Calendar

"Materials informatics: Moving beyond screening via generative machine learning models"

Machine learning already enables the discovery of new materials by providing rapid predictions of properties to complement slower calculations and experiments. However, a persistent criticism of machine learning enabled materials discovery is that new materials are very similar, both chemically and structurally, to previously known materials. This begs the question “Can machine learning ever learn new chemistries and families of materials that differ from those present in the training data?” In this talk, I will describe new generative machine learning approaches that can be used to generate structures that do not yet exist, but are likely to. I will compare generative models including variational autoencoders, generative adversarial networks, and diffusion models which have become standard in machine learning for images. I will describe the unique challenges that we face when using tools of this nature to generate periodic crystalline structures and I’ll also describe how tools such as DiSCoVeR and SMACT can be used in conjunction to ensure chemically reasonable, yet interesting outputs. 

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